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This paper aims to investigate the effect of the economic policy uncertainty (EPU), geopolitical risk (GPR) and climate policy uncertainty (CPU) of USA on Bitcoin volatility from August 2010 to August 2022.

In this paper, the authors have adopted the empirical strategy of Yen and Cheng (2021), who modified volatility model of Wang and Yen (2019), and the authors use an OLS regression with Newey-West error term.

The results using OLS regression with Newey–West error term suggest that the cryptocurrency market could have hedge or safe-haven properties against EPU and geopolitical uncertainty. While the authors find that the CPU has a negative impact on the volatility of the bitcoin market. Hence, the authors expect climate and environmental changes, as well as indiscriminate energy consumption, to play a more important role in increasing Bitcoin price volatility, in the future.

This study has two implications. First, to the best of the authors’ knowledge, the study is the first to extend the discussion on the effect of dimensions of uncertainty on the volatility of Bitcoin. Second, in contrast to previous studies, this study can be considered as the first to examine the role of climate change in predicting the volatility of bitcoin. This paper contributes to the literature on volatility forecasting of cryptocurrency in two ways. First, the authors discuss volatility forecasting of Bitcoin using the effects of three dimensions of uncertainty of USA (EPU, GPR and CPU). Second, based on the empirical results, the authors show that cryptocurrency can be a good hedging tool against EPU and GPR risk. But the cryptocurrency cannot be a hedging tool against CPU risk, especially with the high risks and climatic changes that threaten the environment.

This study aims to elucidate the volatility spillovers among commodities, equities and socially responsible investments, underpinning their dynamic correlations during the economic instability wrought by the COVID-19 pandemic and associated financial crises.

This research quantitatively analyzes volatility transmission across various financial assets from January 2005 to October 2020 by employing the Diebold and Yilmaz (2012) spillover index. The methodology incorporates a temporal examination to capture the evolution of volatility dependencies pre and post the emergence of COVID-19.

The findings indicate substantial volatility spillovers among the assets in question, aligning with the current financialisation of commodity markets and a rise in financial market integration. These spillovers also show variation over time. Notably, the interconnectedness among the assets intensifies during periods of stress. For instance, the total spillover index significantly surpassed 80% toward the end of January 2020, following the onset of the COVID-19 crisis. Furthermore, the results imply that financial markets appear to be segmented.

The findings afford investors a more comprehensive insight into both the character and scale of the interdependencies across a broad array of financial markets. Indeed, grasping the extent to which financial markets are segmented or integrated during times of stress and stability is crucial for investors. Such understanding is key to more accurately evaluating risks, diversifying investment portfolios and devising more efficient hedging strategies.

This study contributes to financial literature by offering a comprehensive investigation into the spillover effects across a diverse set of asset classes during an unprecedented global health crisis, filling a gap in existing research on market behavior against the backdrop of a pandemic-induced financial crisis.

This study aims to investigate the conditional volatility of the Asian stock market concerning Bitcoin and global crude oil price movement.

This study uses the newest Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to examine the conditional volatility of the stock market for Bitcoin and crude oil prices in the Asian perspective. The sample stock market includes Chinese, Indian, Japanese, Malaysian, Pakistani, Singaporean, South Korean and Turkish stock exchanges, with daily time series data ranging from 4 April 2015−31 July 2023.

The outcome reveals the presence of volatility clustering on the return series of crude oil, Bitcoin and all selected stock exchanges of the current study. Secondly, the outcome of DCC, manifests that there is no short-run volatility spillover from crude oil to the Malaysian, Pakistani and South Korean and Turkish stock markets, whereas Chinese, Indian, Japanese, Singapore stock exchanges show the short-run volatility spillover from crude oil in the short run. On the other hand, in the long run, there is a volatility spillover effect from crude oil to all the stock exchanges. Thirdly, the findings suggest that there is no immediate spillover of volatility from Bitcoin to the stock markets return volatility of China, India, Malaysia, Pakistan, South Korea and Singapore. In contrast, both the Japanese and Turkish stock exchanges exhibit a short-term volatility spillover from Bitcoin. In the long term, a volatility spillover effect from Bitcoin is observed in all stock exchanges except for Malaysia. Lastly, based on the outcome of conditional variance, it can be concluded that there was increase in the return volatility of stock exchanges during the period of the COVID-19 pandemic.

The analysis below does not account for the bias induced due to certain small sample properties of DCC-GARCH model. There exists a huge literature that suggests other methodologies for small sample corrections such as the DCC connectedness approach. On the other hand, decisive corollaries of the conclusions drawn above have been made purely based on a comprehensive investigation of eight Asian stock exchange economies. However, there is scope for inclusive examination by considering other Nordic and Western financial markets with panel data approach to get more robust inferences about the reality.

Most of the empirical analysis in this perspective skewed towards the Nordic and Western countries. In addition to that many empirical investigations examine either the impact of crude oil price movement or Bitcoin performance on the stock market return volatility. However, none of the examinations quests the crude oil and Bitcoin together to unearth their implication on the stock market return volatility in a single study, especially in the Asian context. Hence, current investigation endeavours to examine the ramifications of Bitcoin and crude oil price movement on the stock market return volatility from an Asian perspective, which has significant implications for the investors of the Asian financial market.

This study investigates the effect of the day of the week on the volatility of cryptocurrencies. Thus, we reveal investors' perceptions of the day of the week.

The EGARCH model consists of the day of the week for 2019–2022 and the volatility of 11 cryptocurrencies.

Empirical results show that the weekend harms cryptocurrency volatility. Also, there was positive cryptocurrency volatility at the beginning of the week. Our findings show that weekdays and weekends significantly impact cryptocurrency volatility. Besides, cryptocurrency investors are sensitive to market movements, disclosures, and regulations during the week. Holiday mode and cognitive shortcuts may cause cryptocurrency traders to remain passive on weekends.

This study has some limitations. We include 11 cryptocurrencies in the analysis by limiting cryptocurrencies according to market capitalizations. Further studies may analyze a larger sample. In addition, further studies may examine the moderator and mediator effects of other financial instruments.

The empirical results have research, social and practical conclusions from different aspects. Our analysis may contribute to determining trading strategies, risk management, market efficiency, regulatory oversight, and investment decisions in the cryptocurrency market.

The calendar effect in financial markets has extensive literature. However, cryptocurrencies' weekday and weekend effect needs to be adequately analyzed. Besides, studies analyzing cryptocurrency volatility are limited. We contribute to the literature by investigating the impact of days of the week on cryptocurrency volatility with a large sample and current data.

This paper investigates the information content of implied volatilities inferred from individual stock options quoted over-the-counter (OTC). First, we examine whether the implied volatility has better explanatory power than historical volatility for forecasting future realized volatility of the underlying stock return. Next, we analyze the properties of volatility spreads, the difference between implied volatilities and realized volatilities. Using near-the-money options for 10 firms over the sample period from April 2005 to April 2010, we first demonstrate that the implied volatilities for most firms don’t have additional information beyond what are already contained in historical volatilities. However, the implied volatilities with some specific remaining maturities for two firms dominate historical volatilities in explaining the future realized volatilities. Second, we find that during the period before global financial crisis, the implied volatilities are systematically lower than the future realized volatilities whereas this reversal disappears after the year 2008. This finding suggests that there’s a possibility of the risk loving behavior of the investors in OTC individual stock options market during the pre-global crisis period. Finally, through the comparative analysis of the KOSPI200 index options quoted OTC over the same sample period, we conclude that the OTC individual stock options market has distinctive characteristics like the KRW/USD OTC currency options market.

The purpose of this research is to determine whether the trading of equity index futures contracts on the South African Futures Exchange (SAFEX) results in an increase in the volatility of the underlying spot indices. Since equity index futures contracts were first listed in the USA in 1975, various studies have been undertaken to determine whether the volatility of shares in the underlying indices increases as a result of the trading of such futures contracts. These studies have lead to the development of two schools of thought: [a] Trading activity in equity index futures contracts leads to an increase in the volatility of index shares. [b] Trading activity in equity index futures contracts does not lead to an increase in the volatility of the index shares and could in fact lead to greater stability in equity markets. Although some evidence of higher volatility in expiration periods was found, volatility in the expiration periods was not consistently higher than in the corresponding pre‐expiration period.

The purpose of this paper is to amend the New Keynesian Phillips Curve (NKPC) model to include inflation volatility. It provides results on the determinants of inflation volatility and expected inflation volatility for ordinary least squares and autoregressive distributed lags (1,1) models and for change in inflation volatility and change in expected inflation volatility using error correction mechanism (ECM) models. Output gap affects change in expected inflation volatility alone (in the ECM model) and not in the other models. Major determinants of inflation volatility and expected inflation volatility are identified. To the best of the authors knowledge this is the first paper to augment the NKPC to include inflation volatility.

Recent analysis has indicated the importance of inflation volatility for the monetary transmission mechanism in India (Kapur and Behera, 2012). In the analysis of such monetary policy mechanisms the NKPC has proved to be a useful tool. Thus Patra and Ray (2010) for India and Brissimis and Magginas (2008) for the USA find considerable support for the standard NKPC. The purpose of this paper is to synthesize and integrate these two models by extending the standard NKPC framework to include inflation volatility and test its significance for the case of India.

In the case of inflation volatility output gap, lagged output gap and lagged inflation volatility are all insignificant. The level of inflation has a negative significant impact whereas the level of expected inflation has a positive and significant impact. In the case of expected inflation volatility lagged output gap has a negative and significant impact, the price level has a positive and significant impact whereas expected price has a negative and weakly significant impact. ECM reveals change in inflation variability falls significantly with lagged inflation volatility and lagged inflation and less significantly with change in expected inflation. It rises with lagged expected inflation although the coefficient is only weakly significant. Lagged output gap and change in output gap are insignificant.

This paper makes two original contributions. First, it extends the New Keynesian framework to include inflation volatility. Second, it estimates this model for India. To the best of the authors knowledge this is the first paper to make these contributions.

The development of standardized measures of institution‐wide volatility exposures has so far lagged that for measures of asset price and interest‐rate exposure—largely because it is difficult to reconcile the various mathematical models used to value options. Recent mathematical results, however, can be used to construct standardized measures of volatility exposure. We consider here techniques for reconciling “vegas” for financial options valued using stochastic models that may be mathematically inconsistent with each other.

The purpose of this paper is to examine whether stock market liberalization creates excess stock return volatility in the Qatar Exchange (QSC).

The study utilizes two methods, simple analysis of variance and the EGARCH model with dummy variables.

Results reveal no change in market volatility following the partial removal of the restrictions on foreign participation. Results suggest, however, that the degree of persistence in volatility is high, which implies that once volatility increases it remains high over a long run. In addition, conditional volatility tends to rise when the absolute value of the standardized residuals was large. While, contrary to what has been found in the literature, the return volatility seems to be symmetric.

The finding of volatility persistence and clustering might imply an inefficient stock market. Therefore, policy makers should emphasize and direct their attention toward increasing the efficiency of the stock market.

Being able to make predictions about financial market volatility is of special importance to investors and policy makers since it makes available to them a measure of risk exposure in their investments and decisions.

This paper provides a contribution to the empirical literature on stock market volatility. It is the only study, to the authors' knowledge, that investigates the issue of QSC liberalization and volatility. The authors believe that QSC has its own unique characteristics, and the results of the study depend mainly on the market's specific conditions, the quality of its financial institutions and the extent of financial liberalization obtained.

The Chinese stock market is a typical emerging market with special features that are very different from those of mature markets. The objective of this study is to investigate whether and how these features affect the volatility‐volume relation for Chinese stocks.

This paper examines the roles of the number of trades, size of trades, and share volume in explaining the volatility‐volume relation in the Shanghai Stock Exchange with high frequency trade data used.

The results confirm that the volatility‐volume relation is driven mainly by the number of trades on the Chinese stock market. The number of trades explains the volatility‐volume relation better than the size of trades. Furthermore, some results are obtained that differ from those of mature markets, such as the US market. The results show that the second largest sized trades affect the volatility more than other trades on the Chinese market.

The results show that, in the Shanghai Stock Exchange, informed traders camouflage their private information or manipulation behavior through the second largest sized trades. The results may have important implications for work explaining the volatility‐volume relation on the Chinese stock market, further providing a reference by which to regulate emerging markets.